{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-distilbert--distilbert-base-uncased-finetuned-sst-2-english","slug":"distilbert--distilbert-base-uncased-finetuned-sst-2-english","name":"distilbert-base-uncased-finetuned-sst-2-english","type":"finetune","url":"https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english","page_url":"https://unfragile.ai/distilbert--distilbert-base-uncased-finetuned-sst-2-english","categories":["model-training"],"tags":["transformers","pytorch","tf","rust","onnx","safetensors","distilbert","text-classification","en","dataset:sst2","dataset:glue","arxiv:1910.01108","doi:10.57967/hf/0181","license:apache-2.0","model-index","endpoints_compatible","deploy:azure","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-distilbert--distilbert-base-uncased-finetuned-sst-2-english__cap_0","uri":"capability://data.processing.analysis.binary.sentiment.classification.with.distilled.transformer","name":"binary-sentiment-classification-with-distilled-transformer","description":"Classifies English text into binary sentiment categories (positive/negative) using DistilBERT, a 40% smaller and 60% faster distilled variant of BERT that retains 97% of BERT's performance through knowledge distillation. The model was fine-tuned on the Stanford Sentiment Treebank v2 (SST-2) dataset with 67,349 labeled movie review sentences, using a transformer encoder architecture with 6 layers, 12 attention heads, and 768 hidden dimensions. Inference produces logits for both classes with softmax normalization, enabling confidence-scored predictions suitable for production deployments.","intents":["Classify customer reviews or feedback as positive or negative sentiment without building a custom model","Add sentiment analysis to applications with minimal latency and computational overhead","Deploy sentiment classification on edge devices or resource-constrained environments","Benchmark sentiment analysis performance against baseline transformer models"],"best_for":["Teams building customer feedback analysis pipelines with strict latency budgets (<100ms)","Solo developers prototyping sentiment-driven features without ML expertise","Organizations migrating from rule-based sentiment tools to neural approaches","Edge deployment scenarios requiring sub-100MB model footprint"],"limitations":["Binary classification only — cannot distinguish neutral sentiment or multi-class emotions (anger, joy, etc.)","Trained exclusively on movie reviews — domain transfer to product reviews, social media, or technical text may degrade accuracy by 5-15%","English-only model — no multilingual support despite DistilBERT's availability in 100+ languages","Fixed sequence length of 512 tokens — longer documents require truncation or sliding window approaches","No confidence calibration post-training — raw logits may not reflect true probability estimates for out-of-distribution inputs"],"requires":["Python 3.7+","transformers library >= 4.0.0 (HuggingFace)","PyTorch >= 1.9.0 OR TensorFlow >= 2.4.0 (framework choice)","~270MB disk space for model weights (safetensors or PyTorch format)","GPU optional but recommended for batch inference >32 samples"],"input_types":["raw text strings (English, UTF-8 encoded)","pre-tokenized sequences (token IDs as integers)"],"output_types":["logits (raw unnormalized scores, shape [batch_size, 2])","probabilities (softmax-normalized, shape [batch_size, 2])","class labels (0=negative, 1=positive)"],"categories":["data-processing-analysis","text-classification"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-distilbert--distilbert-base-uncased-finetuned-sst-2-english__cap_1","uri":"capability://tool.use.integration.multi.framework.model.export.and.inference","name":"multi-framework-model-export-and-inference","description":"Supports inference and deployment across PyTorch, TensorFlow, ONNX Runtime, and Rust ecosystems through standardized model serialization formats (safetensors, PyTorch pickle, TensorFlow SavedModel). The model can be loaded via HuggingFace transformers library with automatic framework detection, or exported to ONNX for hardware-accelerated inference on CPUs, GPUs, and specialized accelerators (TensorRT, CoreML, WASM). Safetensors format provides secure deserialization without arbitrary code execution, critical for untrusted model sources.","intents":["Deploy the same model across heterogeneous infrastructure (cloud, edge, mobile, browser)","Integrate sentiment analysis into non-Python applications (Rust services, JavaScript frontends, C++ systems)","Optimize inference latency through ONNX quantization and hardware-specific runtimes","Safely load pre-trained models without executing arbitrary Python code during deserialization"],"best_for":["Polyglot teams using multiple programming languages and runtime environments","Organizations requiring model deployment across cloud (Azure, AWS) and on-premise infrastructure","Mobile and browser-based applications needing lightweight inference","Security-conscious teams avoiding pickle-based model loading due to code execution risks"],"limitations":["ONNX export requires manual conversion — not all transformer features (e.g., custom attention patterns) export cleanly","TensorFlow and PyTorch versions may have minor numerical differences in inference outputs due to floating-point precision variations","Rust bindings via candle or tch-rs are community-maintained with less frequent updates than Python transformers library","ONNX quantization (int8) may reduce accuracy by 1-3% depending on calibration dataset"],"requires":["transformers >= 4.0.0 for PyTorch/TensorFlow loading","onnx >= 1.12.0 and onnxruntime >= 1.13.0 for ONNX inference","TensorFlow >= 2.4.0 OR PyTorch >= 1.9.0 (framework-specific)","Optional: candle or tch-rs for Rust inference","Optional: ONNX conversion tools (skl2onnx, tf2onnx)"],"input_types":["PyTorch: torch.Tensor or token IDs as integers","TensorFlow: tf.Tensor or token IDs","ONNX: numpy arrays or ONNX tensor format","Rust: ndarray or tch-rs tensors"],"output_types":["PyTorch: torch.Tensor (logits)","TensorFlow: tf.Tensor (logits)","ONNX: numpy arrays (logits)","Rust: ndarray or tch-rs tensors"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-distilbert--distilbert-base-uncased-finetuned-sst-2-english__cap_2","uri":"capability://memory.knowledge.pre.trained.transformer.weight.reuse.for.transfer.learning","name":"pre-trained-transformer-weight-reuse-for-transfer-learning","description":"Provides frozen or fine-tunable transformer encoder weights pre-trained on English Wikipedia and BookCorpus via masked language modeling, enabling rapid transfer learning for downstream sentiment tasks. The model exposes intermediate layer representations (embeddings, hidden states from all 6 layers) that can be extracted for feature engineering or used as initialization for custom classification heads. Supports parameter-efficient fine-tuning via LoRA or adapter modules without modifying base weights, reducing memory overhead and enabling multi-task learning.","intents":["Fine-tune the model on domain-specific sentiment data (e.g., product reviews, social media) without training from scratch","Extract contextualized embeddings for downstream tasks like semantic similarity or clustering","Implement parameter-efficient fine-tuning (LoRA, adapters) to adapt the model to new domains with <1% additional parameters","Use the model as a feature extractor for ensemble methods or traditional ML classifiers"],"best_for":["ML practitioners with labeled domain-specific datasets (100-10k examples) seeking to improve accuracy","Teams with limited compute budgets requiring efficient fine-tuning without full model retraining","Researchers exploring transfer learning from general-domain to specialized sentiment tasks","Organizations building multi-task systems where shared representations benefit multiple downstream tasks"],"limitations":["Fine-tuning on small datasets (<100 examples) risks overfitting — requires careful regularization and validation","Pre-training on Wikipedia/BookCorpus may not transfer well to colloquial or domain-specific language (e.g., medical sentiment, financial news)","LoRA/adapter modules add inference latency (~5-10%) compared to direct fine-tuning due to additional matrix multiplications","No built-in curriculum learning or data augmentation — practitioners must implement these separately"],"requires":["transformers >= 4.0.0","PyTorch >= 1.9.0 OR TensorFlow >= 2.4.0","peft library >= 0.4.0 for LoRA/adapter support","GPU with >=8GB VRAM for efficient fine-tuning (CPU fine-tuning possible but slow)","Labeled training data (minimum 50-100 examples for meaningful transfer)"],"input_types":["raw text strings for fine-tuning","token IDs (integers) for inference","pre-tokenized sequences with attention masks"],"output_types":["hidden states (shape [batch_size, seq_length, 768]) for feature extraction","pooled representations (shape [batch_size, 768]) for downstream tasks","logits (shape [batch_size, 2]) after fine-tuning with custom head"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-distilbert--distilbert-base-uncased-finetuned-sst-2-english__cap_3","uri":"capability://automation.workflow.batch.inference.with.dynamic.padding.and.batching","name":"batch-inference-with-dynamic-padding-and-batching","description":"Optimizes throughput for processing multiple text samples simultaneously through dynamic padding (padding to max length in batch rather than fixed 512 tokens) and automatic batching via transformers pipeline API. Supports variable-length inputs without wasting computation on padding tokens, reducing latency by 20-40% for typical batches. Integrates with HuggingFace Inference API for serverless batch processing and supports async/streaming inference patterns for real-time applications.","intents":["Process large volumes of reviews or feedback (1k-1M samples) efficiently in batch mode","Minimize latency for real-time sentiment classification in production APIs","Reduce GPU memory usage by dynamically padding to actual sequence lengths","Integrate sentiment analysis into data pipelines without managing tokenization/batching manually"],"best_for":["Data engineering teams processing batch sentiment analysis jobs (daily/hourly ETL)","API developers building low-latency sentiment endpoints with variable request sizes","ML ops teams optimizing GPU utilization and cost in cloud deployments","Researchers benchmarking inference performance across batch sizes and hardware"],"limitations":["Dynamic padding requires variable-length tensor handling — some hardware accelerators (TPUs, older GPUs) may not support efficient variable-length computation","Batching introduces latency variance — first request in batch waits for batch to fill, adding 10-100ms depending on batch size","Pipeline API abstracts tokenization details — fine-grained control over token-level operations requires direct model API usage","No built-in request queuing or priority scheduling — high-priority requests may wait behind large batches"],"requires":["transformers >= 4.0.0","PyTorch >= 1.9.0 OR TensorFlow >= 2.4.0","GPU recommended for batch sizes >32 (CPU inference possible but slow)","Optional: HuggingFace Inference API credentials for serverless batch processing"],"input_types":["list of text strings (variable length)","pre-tokenized sequences with attention masks","streaming text data (for real-time inference)"],"output_types":["batch predictions (list of logits or class labels)","confidence scores (softmax probabilities)","per-sample latency metrics"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-distilbert--distilbert-base-uncased-finetuned-sst-2-english__cap_4","uri":"capability://automation.workflow.model.versioning.and.reproducibility.via.huggingface.hub","name":"model-versioning-and-reproducibility-via-huggingface-hub","description":"Provides versioned model checkpoints, training configuration, and metadata through HuggingFace Model Hub with git-based version control, enabling reproducible deployments and rollback capabilities. Each model version includes training hyperparameters, dataset information (SST-2 split), and performance metrics (accuracy, F1 on validation set), allowing teams to audit model provenance and compare versions. Supports model cards with structured metadata (license: Apache 2.0, task: text-classification, language: en) for discoverability and compliance.","intents":["Track model versions and training configurations for compliance and audit purposes","Reproduce exact model behavior by pinning specific HuggingFace Hub revisions in production","Compare performance across model versions and rollback to previous versions if needed","Discover and evaluate pre-trained models with standardized metadata and performance benchmarks"],"best_for":["Regulated industries (finance, healthcare) requiring model provenance and audit trails","ML teams managing multiple model versions across development, staging, and production","Open-source projects seeking community contributions and model sharing","Researchers publishing reproducible results with versioned model checkpoints"],"limitations":["Git-based versioning adds storage overhead — large models (>1GB) require significant Hub storage quota","No built-in A/B testing framework — teams must implement custom evaluation pipelines to compare versions","Model cards are human-written and not automatically validated — metadata accuracy depends on contributor diligence","Hub access requires internet connectivity — offline deployments require pre-downloading specific versions"],"requires":["HuggingFace account (free tier available)","transformers >= 4.0.0 with Hub integration","Internet connectivity for downloading model versions","Optional: git-lfs for managing large model files locally"],"input_types":["model revision strings (e.g., 'main', 'v1.0', specific commit hashes)","model configuration files (config.json, training_args.bin)"],"output_types":["versioned model checkpoints","training metadata and hyperparameters","performance metrics and evaluation results","model cards with structured metadata"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-distilbert--distilbert-base-uncased-finetuned-sst-2-english__cap_5","uri":"capability://planning.reasoning.zero.shot.and.few.shot.adaptation.via.prompt.engineering","name":"zero-shot-and-few-shot-adaptation-via-prompt-engineering","description":"While the model is fine-tuned for binary sentiment classification, it can be adapted to related tasks (e.g., emotion detection, toxicity classification) through prompt-based approaches or by extracting hidden representations and training lightweight classifiers on new labels. The model's 768-dimensional hidden states serve as rich semantic features for few-shot learning scenarios (5-50 labeled examples), enabling rapid adaptation without full fine-tuning. Supports in-context learning patterns where task descriptions are prepended to input text, though effectiveness depends on semantic similarity to SST-2 domain.","intents":["Adapt the model to new sentiment-related tasks (e.g., emotion classification, sarcasm detection) with minimal labeled data","Extract embeddings for few-shot learning scenarios where only 5-50 labeled examples are available","Implement zero-shot classification by leveraging semantic similarity between task descriptions and model representations","Build multi-task systems where shared representations benefit sentiment and related NLP tasks"],"best_for":["Teams with limited labeled data for new tasks seeking to leverage pre-trained representations","Researchers exploring few-shot learning and transfer learning from sentiment to related domains","Practitioners building rapid prototypes without time for extensive fine-tuning","Organizations adapting models to new languages or domains with minimal annotation effort"],"limitations":["Zero-shot performance degrades significantly for tasks semantically distant from sentiment (e.g., named entity recognition, question answering)","Few-shot learning requires careful selection of training examples — random sampling often underperforms stratified or active learning approaches","Prompt engineering effectiveness is task-dependent and requires manual tuning — no automated prompt optimization","Hidden state extraction adds inference latency (~10-20ms) compared to direct classification"],"requires":["transformers >= 4.0.0","PyTorch >= 1.9.0 OR TensorFlow >= 2.4.0","Optional: scikit-learn for training few-shot classifiers on extracted embeddings","Labeled examples for target task (minimum 5-10 for few-shot, ideally 50-100 for reliable performance)"],"input_types":["raw text strings with task descriptions (for prompt-based approaches)","text samples for embedding extraction","few-shot training examples with new labels"],"output_types":["hidden state embeddings (768-dimensional vectors)","predictions on new task labels","confidence scores for few-shot predictions"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":53,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","transformers library >= 4.0.0 (HuggingFace)","PyTorch >= 1.9.0 OR TensorFlow >= 2.4.0 (framework choice)","~270MB disk space for model weights (safetensors or PyTorch format)","GPU optional but recommended for batch inference >32 samples","transformers >= 4.0.0 for PyTorch/TensorFlow loading","onnx >= 1.12.0 and onnxruntime >= 1.13.0 for ONNX inference","TensorFlow >= 2.4.0 OR PyTorch >= 1.9.0 (framework-specific)","Optional: candle or tch-rs for Rust inference","Optional: ONNX conversion tools (skl2onnx, tf2onnx)"],"failure_modes":["Binary classification only — cannot distinguish neutral sentiment or multi-class emotions (anger, joy, etc.)","Trained exclusively on movie reviews 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